Attio CRM AI Features for Data Enrichment Workflows

Attio CRM AI features for data enrichment are most useful when the workflow starts with resolved inputs, not assumptions. Your AI can summarize a record, recommend a route, or prepare follow-up context. It should not silently convert an old title, an ambiguous company name, or a guessed email address into a CRM fact.

Where AI-assisted CRM workflows need enrichment: attio crm ai features for data enrichment

AI-assisted CRM workflows need enrichment when a record does not contain enough dependable context for the next decision.

A CRM record may have a name, a company name, and a note from a prior conversation. That can be enough for a person to recognize the account. It is often not enough for an automated workflow to determine ownership, segment a list, prepare account research, or draft a useful follow-up.

The useful inputs are usually current work-context fields:

• title
• seniority
• department
• company
• company_domain
• location
• country
• company_profile
• linkedin_url

For company records, firmographic context can include industry, employee count, founded year, annual revenue, funding, and monthly visits. These values can give an AI workflow enough context to describe an account or propose a next step. They do not remove the need for governance.

Keep identity resolution separate from inference.

Identity resolution answers questions such as:

• Is this LinkedIn URL associated with the person on this CRM record?
• Does the supplied work email resolve to a person?
• Does this name-plus-employer combination resolve to a current work record?
• Is this company domain associated with the company record?

Inference comes afterward. An AI CRM workflow can use the resolved fields to suggest that a record belongs in a segment or needs review. It should not present that suggestion as a confirmed fact.

For example, a title can be current and still be too specific for a routing rule. seniority provides a controlled band such as director, vp, or c_level. department provides a controlled value such as sales, engineering, finance, or operations. Those normalized fields are better inputs for rules. The current title remains useful context for the reviewer.

This distinction matters when records are old, incomplete, or copied between systems. A workflow that treats every existing property as verified context can compound bad data. A workflow that marks which values were returned by enrichment, which values came from the CRM, and which values were generated by AI has a clearer audit trail.

Define the CRM decisions the workflow can support

Define the decision before you choose fields, rules, or an AI prompt.

The phrase “AI-assisted CRM operations” can cover several different jobs. Each job needs a narrow set of inputs and a clear outcome. Start with the decision the workflow may support, not with every available enrichment field.

Common decisions include:

• Ownership routing. Use department, seniority, location, and company_domain to recommend the right queue or owner.
• Account prioritization. Use company context such as industry, employee range, funding, or monthly visits as inputs to a reviewable priority rule.
• List segmentation. Use normalized department, seniority, country, and company context to place records into defined segments.
• Research briefs. Use headline, company_profile, current title, and location to prepare a concise account or contact brief.
• Follow-up preparation. Use current employer, title, department, and verified work email context to help a rep review a record before sending anything.

Use normalized values for automation. Use descriptive values for people.

A routing rule based on raw titles tends to grow into a long list of exceptions. “Head of Revenue,” “Revenue Lead,” and “VP, Revenue Operations” may all matter differently to your business, but titles are published text and can vary widely. A rule based on seniority and department is easier to inspect:

• Route sales and vp records for enterprise review.
• Send engineering and director records to a technical evaluation queue.
• Flag executive records whose department cannot be determined.

Keep title in the review view. It helps a person see why a normalized classification may need an exception.

Do not allow every AI recommendation to become a direct write. Changes to a record owner, account tier, lifecycle field, or outreach state can affect pipeline reporting and customer communication. Put those actions behind explicit approval criteria.

A practical pattern is:

1. Resolve the fields needed for a decision.
2. Run deterministic rules on normalized fields.
3. Let AI explain the recommendation or prepare context from the resolved record.
4. Require review for critical field changes or ambiguous inputs.
5. Write approved changes with the prior value retained.

This gives the AI a bounded job. It can help operators work through context. It does not become the unreviewed source of truth for your CRM.

Design a safe enrichment input contract

A safe CRM enrichment input contract defines which identifiers you accept, which fields you request, and what happens when data is not returned.

Start with stable identifiers. For people, use a LinkedIn URL, a work email, or a name plus employer. For companies, use a domain, company name, or LinkedIn URL. The identifier you hold should be stored separately from fields returned during enrichment.

That separation is useful when a record cannot be resolved. You can inspect the original input rather than treating an unchanged input as a new enrichment result.

Request only the fields required for the next decision. This reduces unnecessary writes and makes review simpler.

For example:

• A routing workflow may need title, seniority, department, company, and location.
• A territory workflow may need location and country.
• An account research workflow may need company_profile.
• A follow-up preparation workflow may need linkedin_url, title, company, and headline.

Request email only when the workflow genuinely needs a work address. Work email enrichment is not implied by a general people lookup. It must be requested by name, and it is charged only when an address is found.

When a work email is returned, store its email status beside it. The available values are:

• verified
• probable
• unverified
• risky
• undeliverable
• unknown

Do not treat these statuses as interchangeable. A returned address with a risky, undeliverable, or unknown status should not follow the same automated path as one with a verified status. Your CRM can retain the address for review while preventing it from entering an outreach action.

The field catalogue is closed. If your CRM schema includes a property that is not available from enrichment, reject that request instead of creating an empty property and implying that a lookup occurred.

For people, the available fields are limited to the defined catalogue, including email, linkedin_url, title, seniority, department, location, country, company, company_domain, headline, phone, and company_profile.

The same principle applies to mobile numbers. phone is a mobile number sourced from a LinkedIn profile URL. It is billed only when returned. It has no deliverability verdict and should never be described as verified. A returned number is also not a representation that it is lawful to call. Your calling obligations remain your responsibility.

Connect the workflow through the right entry point

Choose the entry point based on how the enrichment run starts and how much review your process needs.

For reviewed batches, use CSV upload. You can upload a list, map source columns to the fields you want returned, preview cost before the run, and download the enriched file. This suits list cleanup, event follow-up preparation, and controlled imports where an operator should inspect the input first.

For repeatable system-to-system flows, use the REST API at /api/v1. It supports people and company search and enrichment, uses an API key, and provides an OpenAPI document at /api/v1/openapi. See the available API material in the docs.

For an agent-initiated task, use the MCP server at /api/mcp. An agent in a supported MCP client can call tools such as search_people, enrich_people, search_companies, enrich_companies, and get_job. The agent should still operate within the same input contract and approval rules as any other workflow.

Enrichments uses the same credit balance and validation layer across these entry points. The transport changes. Your governance rules should not.

Handle inline and queued API responses correctly

Small requests can return inline. A people or company search asking for no more than 10 results can return immediately. An enrichment request with no more than 5 rows can also return inline.

Larger requests are queued. Requests that ask for email are queued as well. So are requests sent with async: true or Prefer: respond-async.

When a request is inline:

• The API returns HTTP 200.
• results is already populated.
• job.status is terminal.
• There is nothing to poll.

When a request is queued:

• results is null until the job reaches a terminal status.
• Poll GET /api/v1/jobs/{id}.
• Read job.status, not a top-level status field.

The response envelope is always:

{
  "job": {
    "object": "job",
    "id": "job_id",
    "type": "people_enrich",
    "status": "succeeded",
    "fields": ["title", "seniority", "department"],
    "requestedCount": 1,
    "resultCount": 1,
    "creditsUsed": 0.5,
    "error": null,
    "createdAt": "timestamp",
    "startedAt": "timestamp",
    "finishedAt": "timestamp",
    "url": "job_url"
  },
  "results": [
    {
      "position": 0,
      "status": "succeeded",
      "data": {
        "title": "Current title",
        "seniority": "director",
        "department": "sales"
      },
      "evidence": {},
      "error": null
    }
  ],
  "page": {},
  "estimatedCredits": 0.5
}

The important implementation detail is that results contains wrappers. Do not write the wrapper directly into your CRM. Inspect each item’s status, then unwrap data before mapping returned fields to CRM properties.

Job statuses are queued, running, succeeded, empty, failed, and cancelled. Item statuses are separate: pending, running, succeeded, not_found, and failed.

If your integration retries a POST request, send the same Idempotency-Key header as the original request. That makes the retry deduplicated. Without that header, a retry is a new request with a new job and a new hold.

Add review rules before AI-generated actions run

Review rules should handle ambiguity before generated actions affect CRM state.

At minimum, send these cases to a human review path:

• Conflicting identity signals, such as a name and employer that do not align with the returned context.
• Item statuses of not_found or failed.
• Work emails with risky, undeliverable, or unknown status.
• A proposed change to a record owner, lifecycle field, account tier, or outreach state.
• A returned company or person context that conflicts with a protected CRM value.
• A workflow that would replace a manually curated field with a generated recommendation.

Separate published location from normalized geography. location is free text as published. It can contain city, region, country, or a combination of these. country is derived from that string.

Use country when your rule needs a normalized geographic value. Retain location when the published detail matters to a rep, territory reviewer, or routing exception. Do not overwrite a useful location string with a country value just because both refer to geography.

Preserve a traceable record of the change. For fields where your team needs accountability, retain:

• The original CRM value.
• The returned enrichment value.
• The enrichment job time from the response.
• The returned evidence.
• The rule or reviewer that approved the write.
• The AI-generated recommendation, if one was used.

This does not require treating enrichment as permanent truth. It gives an operator enough context to understand why the CRM changed and to reverse a write when needed.

Maintain the workflow as records change

Re-enrich around meaningful lifecycle events instead of overwriting records on a blanket schedule.

Useful triggers can include a new inbound lead, an account entering a target segment, an owner review, a renewal workflow, or a record that has not been checked since a meaningful CRM change. The right trigger depends on the decision the enrichment supports.

Enrichment is live rather than a licensed static database. The same query can legitimately return different data later as public information changes. That is useful for freshness, but it means your update policy matters.

Define field-level behavior:

• Update a current title only when your review rules allow it.
• Keep prior values for fields that affect reporting or ownership.
• Treat a new company domain as a review event when it changes account identity.
• Preserve published location text unless a reviewer approves a replacement.
• Avoid replacing a non-empty CRM value with an unresolved result.

Monitor the workflow itself. Review unresolved inputs, repeated requests, and downstream exceptions. A growing set of not_found results may indicate weak identifiers. Repeated requests may indicate missing idempotency handling or an overly broad trigger. Downstream exceptions can reveal a field mapping that expects a value the enrichment contract does not guarantee.

Credits are charged only for data actually delivered. A lookup that returns nothing costs nothing. Repeated questions inside the cache window cost 0. The pricing page lists plan allowances and credit costs, but your operational control should come from narrow field requests, explicit approval rules, and an append-only view of usage.

A governed AI CRM workflow does not ask enrichment to fill every empty property. It resolves the context needed for a specific decision, records what came back, and makes room for a person when the result is uncertain.